Dr Yi Ma

Self-supervised monocular depth and visual odometry (VO) are often cast as coupled tasks. Accurate depth contributes to precise pose estimation and vice versa. Existing architectures typically exploit stacking convolution layers and long short-term memory (LSTM) units to capture long-range dependencies. However, their intrinsic locality hinders the model from getting the expected performance gain. In this article, we propose a Transformer-based architecture, named Transformer-based self-supervised monocular depth and VO (TSSM-VO), to tackle these problems. It comprises two main components: 1) a depth generator that leverages the powerful capability of multihead self-attention (MHSA) on modeling long-range spatial dependencies and 2) a pose estimator built upon a Transformer to learn long-range temporal correlations of image sequences. Moreover, a new data augmentation loss based on structural similarity (SSIM) is introduced to constrain further the structural similarity between the augmented depth and the augmented predicted depth. Rigorous ablation studies and exhaustive performance comparison on the KITTI and Make3D datasets demonstrate the superiority of TSSM-VO over other self-supervised methods. We expect that TSSM-VO would enhance the ability of intelligent agents to understand the surrounding environments.

—We focus on the signal detection for large quasi-symmetric (LQS) multiple-input multiple-output (MIMO) systems , where the numbers of both service (M) and user (N) antennas are large and N/M → 1. It is challenging to achieve maximum-likelihood detection (MLD) performance with square-order complexity due to the ill-conditioned channel matrix. In the emerging MIMO paradigm termed with an extremely large aperture array, the channel matrix can be more ill-conditioned due to spatial non-stationarity. In this paper, projected-Jacobi (PJ) is proposed for signal detection in (non-) stationary LQS-MIMO systems. It is theoretically and empirically demonstrated that PJ can achieve MLD performance, even when N/M = 1. Moreover, PJ has square-order complexity of N and supports parallel computation. The main idea of PJ is to add a projection step and to set a (quasi-) orthogonal initialization for the classical Jacobi iteration. Moreover, the symbol error rate (SER) of PJ is mathematically derived and it is tight to the simulation results.

Electrochemical carbon dioxide (CO2) reduction reaction (CO2RR) is an attractive approach to deal with the excessive emission of CO2 and to produce valuable fuels and chemicals in a carbon-neutral way. Many efforts have been devoted to boost the activity and selectivity of high-value multicarbon products (C2+) on Cu-based electrocatalysts. However, Cu-based CO2RR electrocatalysts suffer from poor catalytic stability mainly due to the structural degradation and loss of active species under CO2RR condition. To date, most reported Cu-based electrocatalysts present stabilities over dozens of hours, which limits the advance of Cu-based electrocatalysts for CO2RR. Here, a porous chlorine-doped Cu electrocatalyst is reported and exhibits high C2+ Faradaic efficiency (FE) of 53.8 % at-1.00 V versus reversible hydrogen electrode (VRHE). Importantly, the catalyst exhibited an outstanding catalytic stability in long-term electrocatalysis over 240 hours. Experimental results show that the chlorine-induced stable cationic Cu 0-Cu + species and the well-preserved structure with abundant active sites are found to be critical to maintain the high FE of C2+ in the long-term run of electrochemical CO2 reduction.

In this paper, the Hermite polynomials are employed to study linear approximation models of narrowband multiantenna signal reception (i.e., MIMO) with low-resolution quantizations. This study results in a novel linear approximation using the second-order Hermite expansion (SOHE). The SOHE model is not based on those assumptions often used in existing linear approximations. Instead, the quantization distortion is characterized by the second-order Hermite kernel, and the signal term is characterized by the first-order Hermite kernel. It is shown that the SOHE model can explain almost all phenomena and characteristics observed so far in the low-resolution MIMO signal reception. When the SOHE model is employed to analyze the linear minimum-mean-square-error (LMMSE) channel equalizer, it is revealed that the current LMMSE algorithm can be enhanced by incorporating a symbol-level normalization mechanism. The performance of the enhanced LMMSE algorithm is demonstrated through computer simulations for narrowband MIMO systems in Rayleigh fading channels.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

Consider robot swarm wireless networks where mobile robots offload their computing tasks to a computing server located at the mobile edge. Our aim is to maximize the swarm lifetime through efficient exploitation of the correlation between distributed data sources. The optimization problem is handled by selecting appropriate robot subsets to send their sensed data to the server. In this work, the data correlation between distributed robot subsets is modelled as an undirected graph. A least-degree iterative partitioning (LDIP) algorithm is proposed to partition the graph into a set of subgraphs. Each subgraph has at least one vertex (i.e., subset), termed representative vertex (R-Vertex), which shares edges with and only with all other vertices within the subgraph; only R-Vertices are selected for data transmissions. When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel. For independent fading channels, the max-min principle can be incorporated into the proposed approach to achieve the best performance.

—The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.

—The synergistic amalgamation of sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) technologies can be exploited for improving spectral efficiency and providing enhanced wireless services to massive users. In this case, however, channel estimation is a burning issue with the increasing number of users and/or antennas. To tackle this problem, we propose a novel non-coherent transmission scheme for SCMA, referred to as NC-SCMA. In the proposed NC-SCMA, each user first maps its binary data to sparse codewords, and then perform differential modulation on the non-zero dimensions. Upon receiving all users' signals, we leverage the channel hardening effect to carry out differential demodulation and multiuser detection without any instantaneous channel state information. In addition, the design of the sparse codebooks in the NC-SCMA system is investigated with the aid of the pair-wise probability. Numerical results demonstrate the superiority of the proposed technique over the benchmark scheme in terms of bit error rate performance.

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By this means, the computational complexity of COP is made independent to the size of multi-antenna (i.e., MIMO) networks. Instead, it is related to the size of symbol constellation. Through widely linear transform, it is shown that COP has its complexity flexibly scalable in the constellation domain to achieve a good complexityperformance tradeoff. Our computer simulations show that COP can offer very comparable performance with the optimum VP in small MIMO systems. Moreover, it significantly outperforms current sub-optimum VP approaches (such as degree-2 VP) in large MIMO whilst maintaining much lower computational complexity.

—Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-information at the transmitter (CSIT), the proposed approach is proven to ensure maximized URLLC users. Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC users without compromising the individual user reliability by using a pessimistic evaluation of the channel gain. It is demonstrated , through numerical results, that the proposed approach can significantly improve the user capacity and the transmit-power efficiency in Rayleigh fading channels. With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.

This letter presents a novel opportunistic cooperative positioning approach for orthogonal frequency-division multiple access (OFDMA) systems. The basic idea is to allow idle mobile terminals (MTs) opportunistically estimating the arrival timing of the training sequences for uplink synchronization from active MTs. The major advantage of the proposed approach over state-of-the-arts is that the positioning-related measurements among MTs are performed without the paid of training overhead. Moreover, Cramer-Rao lower bound (CRLB) is utilized to derive the positioning accuracy limit of the proposed approach, and the numerical results show that the proposed approach can improve the accuracy of non-cooperative approaches with the a-priori stochastic knowledge of clock bias among idle MTs.

This paper aims to investigate an intra-cell overlay opportunistic spectrum sharing scheme by employing 1-bit feedback beamforming. The work of interests is that base station broadcasts independent signal messages to two relay stations (RS-1 and RS-2). RS-2 decodes the signal messages in subcell 2 and attempts to share the spectrum of sub-cell 1 for its own transmission. For this reason, RS-2 makes a deal with RS-1 in sub-cell 1 to help RS-1 send its signal messages. As presented in the paper, by employing 1-bit feedback transmit beamforming, RS-2 can further improve RS-1's achievable rate and automatically eliminate the interference from RS-2 to subcell 1. Meanwhile, the achievable sum-rate upper bound of RS-2 is also analyzed. © VDE VERLAG GMBH.

In this paper, single-input multiple-output (SIMO) system when employing massive binary array-receiver has been investigated while constructive noise has been observed in the single user system to detect the higher-order QAM modulated signals. To fully understand the interesting phenomenon, mathematical model has been established and analyzed in this paper. Theorems of the signal detectability are studied to understand the best operating signal-to-noise ratio (SNR) range based on the error behaviours of the single user SIMO system. Within the observation and analysis, a novel new multiuser SIMO with binary array-receiver structure has been proposed and can be considered as a solution to deal with the high complexity problem that the traditional model has when using maximum likelihood (ML) detection. The key idea of this approach is to set up the multiuser multiple-input multiple-output (MIMO) model into a frequency division multiple access (FDMA) scenario and regard each user as single user SIMO to achieve the goal of decreasing the exponentially increased complexity of ML detection method to the number of users. It is shown by numerical results that each user in this system can achieve a promising error behaviour in the specific best operating SNR range.

This paper presents a novel frequency-domain energy detection scheme based on extreme statistics for robust sensing of OFDM sources in the low SNR region. The basic idea is to exploit the frequency diversity gain inherited by frequency selective channels with the aid of extreme statistics of the differential energy spectral density (ESD). Thanks to the differential stage the proposed spectrum sensing is robust to noise uncertainty problem. The low computational complexity requirement of the proposed technique makes it suitable for even machine-to-machine sensing. Analytical performance analysis is performed in terms of two classical metrics, i.e. probability of detection and probability of false alarm. The computer simulations carried out further show that the proposed scheme outperforms energy detection and second order cyclostationarity based approach for up to 10 dB gain in the low SNR range. © 2011 IEEE.

The paper presents a time-difference-of-arrival (TDOA) position estimation algorithm for indoor positioning in the present of clock drift in a mobile terminal. Then, a new Cramér-Rao bound is derived as a benchmark of the algorithm. The simulation results show that an acceptable positioning accuracy can be achieved when at least five access points in wireless local area networks are involved in positioning. Moreover, when the clock drift or the TDOA is considerably large, the proposed algorithm outperforms the algorithm without considering the clock drift. © VDE VERLAG GMBH.

The aim of this article is to share a novel concept termed pseudo pilot, which offers a simple and efficient approach of non-pilot-assisted channel estimation. Our key idea is to transfer the uncertainty of several payload symbols into the uncertainty of symbol interleavers by employing a bank of interleavers at the transmitter. Those uncertainty-transferred symbols serve as pseudo pilots for the receiver to perform channel estimation. The uncertainty of symbol interleavers is then removed in the procedure of decoding. Performance and scalability of the pseudo pilot technique are evaluated through both theoretical analysis and computer simulations.

This paper presents a novel approach for mobile positioning in IEEE 802.11a wireless LANs with acceptable computational complexity. The approach improves the positioning accuracy by utilizing the time and frequency domain channel information obtained from the orthogonal frequency-division multiplexing (OFDM) signals. The simulation results show that the proposed approach outperforms the multiple signal classification (MUSIC) algorithm, Ni's algorithm and achieve a positioning accuracy of 1 m with a 97% probability in an indoor scenario.

This work aims to handle the joint transmitter and noncoherent receiver optimization for multiuser single-input multiple-output (MU-SIMO) communications through unsupervised deep learning. It is shown that MU-SIMO can be modeled as a deep neural network with three essential layers, which include a partially-connected linear layer for joint multiuser waveform design at the transmitter side, and two nonlinear layers for the noncoherent signal detection. The proposed approach demonstrates remarkable MU-SIMO noncoherent communication performance in Rayleigh fading channels.

This paper presents two contributions towards incremental decode-forward relaying over asymmetric fading channels. One is about the outage probability of incremental relay network accommodating i.n.d. cooperative paths. Our contribution is mainly on formulating a closed-form of the outage probability through employment of the Inverse Laplace Transform and Eular Summation. The other is about the proposal of transmit-power efficient relay-selection strategy through exploitation of the relationship between position of relays and the outage probability.

This letter proposes a novel carrier frequency offset (CFO) estimation method for generalized multicarrier code-division multiple access systems in unknown frequency-selective channels utilizing hidden pilots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear regression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator. Simulation results are presented together with the theoretical analysis, and a good match between them is observed.

Cooperative communications can exploit distributed spatial diversity gain to improve link performance. When the message is coded at a low rate, source and relay can send different parts of a codeword to destination. This is referred to as the coded cooperation. In this paper, we propose two novel coded cooperation schemes for three-node relay networks, i.e., adaptive coded cooperation and ARQ-based coded cooperation. The former one needs the channel quality information available at source. The codeword splits adaptively to minimize the overall BER. The latter one is devised for relay network with erasure. In the first time slot, source sends a high-rate sub-codeword. Once destination reports the decoding errors, either source or relay can send one or two new bits selected from the mother codeword. Unlike random rateless erasure codes, such as Fountain code, the proposed scheme is based on the deterministic code generator and puncture pattern. It is experimentally shown that the proposed scheme can offer improved throughput in comparison with the conventional approach.

Previous work about cooperative localization in cellular networks usually consider a centralized processor (CP) is available for location estimation. This paper consider cooperative localization in a distributed base station (BS) scenario, where there is no CP, and the distributed BSs are responsible for location estimation. In this scenario, Global Positioning System (GPS) enable mobile terminals (MTs), i.e., located MTs, are employed as reference nodes. Then, several located MTs can help to find the locations of an un-located MT, by estimating the distance between the un-located MT using received signal strength techniques. Two localization approaches are proposed, the first approach requires only one BS to collect all the assistance information for localization and estimate the location. The second approach distribute the location estimation task to several BSs. The communication overhead between distributed BSs are investigated for these two approaches. Moreover, by taking into account the effect of imperfect location knowledge of the located MTs, the accuracy limits of both approaches are derived. The simulation results shows that compared with the first approach, the second approach can reduce the communication overhead between distributed BSs with the paid of accuracy. © 2011 IEEE.

In this paper, a novel spatially non-stationary channel model is proposed for link-level computer simulations of massive multiple-input multiple-output (mMIMO) with extremely large aperture array (ELAA). The proposed channel model allows a mix of none line-of-sight (NLoS) and LoS links between a user and service antennas. The NLoS/LoS state of each link is characterized by a binary random variable, which obeys a correlated Bernoulli distribution. The correlation is described in the form of an exponentially decaying window. In addition, the proposed model incorporates shadowing effects which are non-identical for NLoS and LoS states. It is demonstrated, through computer emulation, that the proposed model can capture almost all spatially non-stationary fading behaviors of the ELAA-mMIMO channel. Moreover, it has a low implementational complexity. With the proposed channel model, Monte-Carlo simulations are carried out to evaluate the channel capacity of ELAAmMIMO. It is shown that the ELAA-mMIMO channel capacity has considerably different stochastic characteristics from the conventional mMIMO due to the presence of channel spatial non-stationarity.

A pilot-based spectrum sensing approach in the presence of unknown timing and frequency offset is proposed in this paper. Our major idea is to utilize the second order statistics of the received samples, such as autocorrelation, to avoid the frequency offset problem. Base on the property of the pilot symbols, where the different symbol blocks usually have the same pilot symbols, some nonzero terms will appear in the frequency domain. To test the proposed approach, computer simulations are carried out for the typical Orthogonal frequency-division multiplexing (OFDM) system. It is observed that the proposed approach always outperforms the classic time domain Neyman-Pearson approach at least 4dB. Moreover, the proposed approach get the same performance as the weighted linear combination based approach when the transmitted data block size is equal to 2048, while a small computational cost is keep at the same time. Therefore, it can be said that the proposed approach can achieve a good trade-off between reliability, latency and the computational cost, when the transmitted data block size of the primary system is larger than 1000. © VDE VERLAG GMBH.

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i.d. complex Gaussian channels and spatially-correlated channels.

Underlay cognitive beamforming allows secondary transmitters to suppress interferences to the primary users, whilst maintain their own quality of services. This paper aims at investigating joint power and interference trade-off inherent in the underlay cognitive beamforming scheme. It is shown that the work of interests leads to a non-convex optimization problem, which can be resolved by employing the second-order cone programming. It is theoretically proved that introducing zero-interference to the primary user does not always lead to the system optimality; moreover, we exhibit two conditions, for which the interference should be treated as noise in order to maximize the sum-rate of the considered beamforming system. © VDE VERLAG GMBH.

In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users’ CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUIcancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

Multiuser multiple-input multiple-output (MUMIMO) nonlinear precoding techniques face the problem of poor computational scalability to the size of the network. In this paper, the fundamental problem of MU-MIMO scalability is tackled through a novel signal-processing approach, which is called degree-2 vector perturbation (D2VP). Unlike the conventional VP approaches that aim at minimizing the transmit-to-receive energy ratio through searching over an N-dimensional Euclidean space, D2VP shares the same target through an iterative-optimization procedure. Each iteration performs vector perturbation over two optimally selected subspaces. By this means, the computational complexity is managed to be in the cubic order of the size of MUMIMO, and this mainly comes from the inverse of the channel matrix. In terms of the performance, it is shown that D2VP offers comparable bit-error-rate to the sphere encoding approach for the case of small MU-MIMO. For the case of medium and large MU-MIMO when the sphere encoding does not apply due to unimplementable complexity, D2VP outperforms the lattice reduction VP by around 5-10 dB in Eb/No and 10-50 dB in normalized computational complexity.

This letter presents a new posterior Cramér-Rao bound (PCRB) for inertial sensors enhanced mobile positioning, which performs hybrid data fusion of parameters including position estimates, pedestrian step size, pedestrian heading, and the knowledge of random walk motion model. Moreover, a non-matrix closed form of the PCRB is derived without position estimates. Finally, our numerical results show that when the accuracy of step size and heading measurements is high enough, the knowledge of random walk model becomes redundant.

This paper presents a parallel computing approach that is employed to reconstruct original information bits from a non-recursive convolutional codeword in noise, with the goal of reducing the decoding latency without compromising the performance. This goal is achieved by means of cutting a received codeword into a number of sub-codewords (SCWs) and feeding them into a two-stage decoder. At the first stage, SCWs are decoded in parallel using the Viterbi algorithm or equivalently the brute force algorithm. Major challenge arises when determining the initial state of the trellis diagram for each SCW, which is uncertain except for the first one; and such results in multiple decoding outcomes for every SCW. To eliminate or more precisely exploit the uncertainty, an Euclidean-distance minimization algorithm is employed to merge neighboring SCWs; and this is called the merging stage, which can also run in parallel. Our work reveals that the proposed two-stage decoder is optimal and has its latency growing logarithmically, instead of linearly as for the Viterbi algorithm, with respect to the codeword length. Moreover, it is shown that the decoding latency can be further reduced by employing artificial neural networks for the SCW decoding. Computer simulations are conducted for two typical convolutional codes, and the results confirm our theoretical analysis.

Quantization is the characterization of analogueto- digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

The Wireless Hybrid Enhanced Mobile Radio Estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.

Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is one of the limiting factors needs to be considered during task offloading. In this paper, multi-task offloading strategies have been investigated to improve device energy efficiency. Correlations among tasks in time domain as well as task domain are proposed to be employed to reduce the number of tasks to be transmitted to MEC. Furthermore, a binary decision tree based algorithm is investigated to jointly optimize the mobile device clock frequency, transmission power, structure and number of tasks to be transmitted. MATLAB based simulation is employed to demonstrate the performance of our proposed algorithm. It is observed that the proposed dynamic multi-task offloading strategies can reduce the total energy consumption at device along various transmit power versus noise power point compared with the conventional one.

In this paper, we propose a rate-adaptive bit and power loading approach for the OFDM-based relaying communications. The cooperative relay operates in the half-duplex amplify-and-forward mode. The source and the relay has the separate power constraints. The maximum-ratio combining is employed at -the destination for maximizing the received SNR. Assuming the perfect channel knowledge available at all nodes, the proposed approach is to maximize the throughput (the number of bits/symbol) at the given power constraint and the target link performance. Unlike the water-filling method, the proposed approach does not need the iterative loading process, and can otTer the sub-optimum performance. Computer simulations are used to test the proposed approach for various scenarios with respect to the relay location or the distributed power allocation. © 2008 IEEE.

This paper consider cooperative localization in cellular networks. In this scenario, several located mobile terminals (MTs) are employed as reference nodes to find the location of an un-located MT. The located MTs sent training sequences in the uplink, then the un-located MT perform distance estimation using received signal strength techniques. The localization accuracy of the un-located MT is characterized in terms of squared position error bound (SPEB) [1]. By taking into account the imperfect a priori location knowledge of the located MTs, the SPEB is derived in a closed-form. The closed-form indicate that the effect of the imperfect location knowledge on SPEB is equivalent to the increase of the variance of distance estimation. Moreover, based on the obtained closed-form, a MT selection scheme is proposed to decrease the number of located MTs sending training sequences, thus reduce the training overhead for localization. The simulation results show that the proposed scheme can reduce the training overhead with the paid of accuracy. and with the same training overhead, the accuracy of the proposed scheme is better than that of random selection. © 2011 IEEE.

Considering a densely populated area where a mobile device, with a single RF chain, shares its message with a set of mobile devices through narrowband mmWave channel, an analogue-beam splitting approach is proposed to achieve a good capacity and coverage trade-off. The proposed approach aims at maximizing the capacity of the mmWave multicast channel through antenna-element grouping and adaptive phase shifting, which takes into account of the inter-beam interference. When receivers are randomly distributed on a circle centered at the transmitter, according to the uniform distribution, it is found that the impact of inter-beam interference on the channel capacity can be negligibly small, and thus the analoguebeam splitting approach can be largely simplified in practice. Computer simulations are carried out to elaborate our theoretical study and demonstrate considerable advantages of the proposed analogue-beam splitting approach.

Spectrum sensing is one of key enabling techniques to advanced radio technologies such as cognitive radios and ALOHA. This paper presents a novel non-cooperative spectrum sensing approach that can achieve a good trade-off between latency, reliability and computational complexity. Our major idea is to exploit the first-order cyclostationarity of the primary user's signal to reduce the noise-uncertainty problem inherent in the conventional energy detection approach. It is shown that the proposed approach is suitable for detecting the primary user's activity in the interweave paradigm of cognitive spectrum sharing, while the active primary user is periodically sending training sequence. Computer simulations are carried out for the typical IEEE 802.11g system. It is observed that the proposed approach outperforms both the energy detection and the second-order cyclostationarity approach when the observation period is more than 10 frames corresponding to 0.56 ms. ©2010 IEEE.

The aim of this paper is to handle the multifrequency synchronization problem inherent in orthogonal frequency-division multiple access (OFDMA) uplink communications, where the carrier frequency offset (CFO) for each user may be different, and they can be hardly compensated at the receiver side. Our major contribution lies in the development of a novel OFDM receiver that is resilient to unknown random CFO thanks to the use of a CFO-compensator bank. Specifically, the whole CFO range is evenly divided into a set of sub-ranges, with each being supported by a dedicated CFO compensator. Given the optimization for CFO compensator a NP-hard problem, a machine deep-learning approach is proposed to yield a good sub-optimal solution. It is shown that the proposed receiver is able to offer inter-carrier interference free performance for OFDMA systems operating at a wide range of SNRs.

This letter presents a reduced-complexity algorithm for coordinated beamforming aimed at solving the multicell downlink max-min signal-to- interference-plus-noise problem under per-base-station power constraints. It is shown that the proposed algorithm can achieve close performance to the optimum algorithm with faster convergence and lower complexity. © 2014 IEEE.

One of the major challenges of Cellular network based localization techniques is lack of hearability between mobile terminals (MTs) and base stations (BSs), thus the number of available anchors is limited. In order to solve the hearability problem, previous work assume some of the MTs have their location information via Global Positioning System (GPS). These located MT can be utilized to find the location of an un-located MT without GPS receiver. However, its performance is still limited by the number of available located MTs for cooperation. This paper consider a practical scenario that hearability is only possible between a MT and its home BS. Only one located MT together with the home BS are utilized to find the location of the un-located MT. A hybrid cooperative localization approach is proposed to combine time-of-arrival and received signal strength based fingerprinting techniques. It is shown in simulations that the proposed hybrid approach outperform the stand-alone time-of-arrival techniques or received signal strength based fingerprinting techniques in the considered scenario. It is also found that the proposed approach offer better accuracy with larger distance between the located MT and the home BS. © 2011 IEEE.

This paper presents an overview of preliminary results of investigations within the WHERE2 Project [1] on identifying promising avenues for location aided enhancements to wireless communication systems. The wide ranging contributions are organized according to the following targeted systems: cellular networks, mobile ad hoc networks (MANETs) and cognitive radio. Location based approaches are found to alleviate significant signaling overhead in various forms of modern communication paradigms that are very information hungry in terms of channel state information at the transmitter(s). And this at a reasonable cost given the ubiquitous availability of location information in recent wireless standards or smart phones. Location tracking furthermore opens the new perspective of slow fading prediction. © VDE VERLAG GMBH.

This paper proposes a novel carrier frequency offset (CFO) estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi- lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator.

In this paper, a novel approach, namely realcomplex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals. By RCHM, we mean that user terminals transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing trade-off that minimizes the required transmit-power given a sum-rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average biterror- rate as well as extensive computer simulations considering the case with single or multiple base-stations.

In this paper, a cooperative iterative water-filling approach is investigated for two-user Gaussian interference channel. State-of-the-art approaches only maximize the individual user's own rate and always model interference as noise. Our proposed approach establishes user cooperation through sharing network side information. It iteratively maximizes the sum-rate of both users subject to distributed power constraint. Interference is optimally regarded as message or noise. Three efficient rate-sharing schemes are also investigated between two users based on priority. Numerical results are performed in frequency-selective environment. It is observed that the proposed approach offers significantly performance improvement in comparison with conventional iterative water-filling approaches.

Multi-access edge computing for mobile computingtask offloading is driving the extreme utilization of available degrees of freedom (DoF) for ultra-reliable low-latency downlink communications. The fundamental aim of this work is to find latency-constrained transmission protocols that can achieve a very-low outage probability (e.g. 0:001%). Our investigation is mainly based upon the Polyanskiy-Poor-Verd´u formula on the finite-length coded channel capacity, which is extended from the quasi-static fading channel to the frequency selective channel. Moreover, the use of a suitable duplexing mode is also critical to the downlink reliability. Specifically, time-division duplexing (TDD) outperforms frequency-division duplexing (FDD) in terms of the frequency diversity-gain. On the other hand, FDD takes the advantage of having more temporal DoF in the downlink, which can be exchanged into the spatial diversity-gain through the use of space-time coding. Numerical study is carried out to compare the reliability between FDD and TDD under various latency constraints.

Doubly differential modem turns out to be a promising technology for coping with unknown frequency offsets with the pay of signal-to-noise ratio (SNR). In this paper, we propose to compensate the SNR loss by employing the detection-forward cooperative relay. The receiver can employ two kind of combiners to attain the achievable spatial diversity-gain. Performance analysis is carefully investigated for the Rayleigh-fading channel. It is shown that the SNR-compensation is satisfied for the large-SNR range.

In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signalling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex (HD) relaying case, and provide the transmission power and relay location optimization strategy to further enhance system performances. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check based selective decode-and-forward (S-DF) relaying and threshold based SDF relaying in terms of outage probability and bit-error-rate. Moreover, the performances with optimal power allocation are better than those with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.

In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) for optimizing the throughput of mobile users is investigated for UAV networks. This problem is formulated as a time-varying mixed-integer non-convex programming (MINP) problem, which is challenging to find an optimal solution in a short time with conventional optimization techniques. Hence, we propose an actor-critic-based (AC-based) deep reinforcement learning (DRL) method to find near-optimal UAV positions at every moment. In the proposed method, the process searching for the solution iteratively at a particular moment is modeled as a Markov decision process (MDP). To handle infinite state and action spaces and improve the robustness of decision process, two powerful neural networks (NNs) are configured to evaluate the UAV position adjustments and make decisions, respectively. Compared with heuristic, sequential least-squares programming and fixed methods, Simulation results have shown that the proposed method outperforms in terms of the throughput at every moment in UAV networks.

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity. Index Terms SCMA, codebook design, deep neural network, autoencoder, multi-task learning.

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalizationand- learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many cochannel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.

The aim of this letter is to exhibit some advantages of using real constellations in large multi-user (MU) MIMO systems. It is shown that a widely linear zero-forcing (WLZF) receiver with M-ASK modulation enjoys a spatial-domain diversity gain, which linearly increases with the MIMO size even in fully- and over-loaded systems. Using the decision of WLZF as the initial state, the likelihood ascent search (LAS) achieves nearoptimal BER performance in fully-loaded large MIMO systems. Interestingly, for coded systems, WLZF shows a much closer BER to that of WLZF-LAS with a gap of only 0:9-2 dB in SNR.

—In vehicle-to-infrastructure (V2I) networks, a cluster of multi-antenna access points (APs) can collaboratively conduct transmitter beamforming to provide data services (e.g., eMBB or URLLC). The collaboration between APs effectively forms a networked linear antenna-array with extra-large aperture (i.e., network-ELAA), where the wireless channel exhibits spatial non-stationarity. Major contribution of this work lies in the analysis of beamforming gain and radio coverage for network-ELAA non-stationary Rician channels considering the AP clustering. Assuming that: 1) the total transmit-power is fixed and evenly distributed over APs, 2) the beam is formed only based on the line-of-sight (LoS) path, it is found that the beamforming gain is concave to the cluster size. The optimum size of the AP cluster varies with respect to the user's location, channel uncertainty as well as data services. A user located farther from the ELAA requires a larger cluster size. URLLC is more sensitive to the channel uncertainty when comparing to eMBB, thus requiring a larger cluster size to mitigate the channel fading effect and extend the coverage. Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.

In this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neural network (ANN)-assisted MIMO signal detection. Our basic idea lies in the discovery and exploitation of the training-sample orthogonality between the current training epoch and past training epochs. Unlike the conventional SGD that updates the neural network simply based upon current training samples, O-SGD discovers the correlation between current training samples and historical training data, and then updates the neural network with those uncorrelated components. The network updating occurs only in those identified null subspaces. By such means, the neural network can understand and memorize uncorrelated components between different wireless channels, and thus is more robust to wireless channel variations. This hypothesis is confirmed through our extensive computer simulations as well as performance comparison with the conventional SGD approach.

Orthogonal relay based cooperative communication enjoys distributed spatial diversity gain at the price of spectral efficiency. This work aims at improving the spectral efficiency for orthogonal opportunistic decode-and-forward (DF) relaying through employment of novel adaptive modulation scheme. The proposed scheme allows source and relay to transmit information in different modulation formats, while the MAP receiver is employed at destination for the diversity combining. Given the individual power constraint and target bit-error-rate (BER), the proposed scheme can significantly improve the spectral efficiency in comparison with the non-adaptive DF relaying and adaptive direct transmission.

Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence (or machine learning) could bring to us, particularly on the physical layer design; and what penalties there may have? These questions motivate a fundamental rethinking of the wireless modem design in the artificial intelligence era. Through several physical-layer case studies, we argue for a significant role that machine learning could play, for instance in parallel error-control coding and decoding, channel equalization, interference cancellation, as well as multiuser and multiantenna detection. In addition, we will also discuss the fundamental bottlenecks of machine learning as well as their potential solutions in this paper.